Systems and methods for image reconstruction

This application is a Continuation of International Application No. PCT/CN2022/078092, filed on Feb. 25, 2022, which claims priority of Chinese Patent Application No. 202110219969.8, filed on Feb. 26, 2021, and the contents of each of which are hereby incorporated by reference.

The present disclosure generally relates to image processing, and in particular, to systems and methods for image reconstruction.

Imaging devices generally include a first portion for data acquisition and transmission, and a second portion for data processing (e.g., image reconstruction). Efficient image reconstruction generally needs high-performance processing devices. Rapid development of imaging devices puts forward higher requirements for the processing devices, increasing the cost and/or power consumption of the processing devices. Therefore, it is desired to provide systems and methods for reconstructing images efficiently and with low cost.

According to one aspect of the present disclosure, a method for image reconstruction is provided. The method may include one or more of the following operations: obtaining image raw data; determining, based on a complexity of a reconstruction task of the image raw data and processing capacities of one or more workstations, from the one or more workstations, one or more hardware resources for processing the image raw data; generating, based on the image raw data and the one or more hardware resources, one or more sub tasks of the reconstruction task; assigning the one or more sub tasks to the one or more hardware resources; obtaining one or more intermediate results corresponding to the one or more sub tasks, the one or more intermediate results being generated by the one or more hardware resources; and generating, based on the one or more intermediate results, a reconstruction result of the image raw data.

According to one aspect of the present disclosure, a method for image reconstruction is provided. The method may include one or more of the following operations: determining, according to a capacity quantization standard, processing capacities of local resources; providing the processing capacities for an image reconstruction service platform; receiving one or more sub tasks and image raw data relating to the one or more sub tasks from the image reconstruction service platform; generating one or more intermediate results by performing, based on the image raw data, the one or more sub tasks; and providing the one or more intermediate results for the image reconstruction service platform.

According to one aspect of the present disclosure, a method for image reconstruction is provided. The method may include one or more of the following operations: obtaining, by a target second node of the plurality of second nodes, first standardized processing capacities of one or more nodes of the plurality of first nodes and/or the plurality of second nodes, based on first image raw data generated by an image scanning process, wherein the target second node has a service request for image reconstruction; generating, by the target second node, based on the first image raw data and the first standardized processing capacities, a first reconstruction task including one or more first sub tasks; assigning, by the target second node, the one or more first sub tasks to at least a portion of the one or more nodes; receiving, by the at least a portion of the one or more nodes, the one or more first sub tasks and the first image raw data; generating, by the at least a portion of the one or more nodes, one or more first intermediate results by performing, based on the first image raw data, the one or more first sub tasks; and providing, by the at least a portion of the one or more nodes, the one or more first intermediate results for the target second node.

According to another aspect of the present disclosure, a system for image reconstruction is provided. The system may include an image reconstruction service platform, a plurality of service receiver nodes, and a plurality of service provider nodes. The image reconstruction service platform may be configured to: obtain, from at least one of the plurality of service receiver nodes, image raw data; determine, based on a complexity of a reconstruction task of the image raw data, processing capacities of the plurality of service receiver nodes, and processing capacities of the plurality of service provider nodes, from the plurality of service receiver nodes and the plurality of service provider nodes, one or more hardware resources for processing the image raw data; determine, based on the image raw data and the one or more hardware resources, one or more sub tasks of the reconstruction task; assign the one or more sub tasks to the one or more hardware resources; obtain one or more intermediate results corresponding to the one or more sub tasks, the one or more intermediate results being generated by the one or more hardware resources; and generate, based on the one or more intermediate results, a reconstruction result of the image raw data.

According to another aspect of the present disclosure, an image reconstruction apparatus configured as a first node of a distributed system for image reconstruction is provided. The apparatus may include a computing device, and a service platform implemented on the computing device. The service platform may include a hardware resource management module, configured to determine, according to a capacity quantization standard, first standardized processing capacities of hardware resources available for the service platform; and a control and data transmission interface, configured to receive one or more first sub tasks and first image raw data, generate one or more first intermediate results corresponding to the one or more first sub tasks, and provide the one or more first intermediate results. The one or more first sub tasks may include a sub task assigned by a second node of the distributed system, and the first image raw data may include image raw data transmitted from the second node.

According to another aspect of the present disclosure, a system for image reconstruction is provided. The system may include at least two second nodes that are in communication with each other; or at least one second node and a plurality of first nodes. Each first node of the plurality of first nodes or each second node may include an apparatus including: a computing device, and a service platform implemented on the computing device. The service platform may include a hardware resource management module, configured to determine, according to a capacity quantization standard, first standardized processing capacities of hardware resources available for the service platform; and a control and data transmission interface, configured to receive one or more first sub tasks and first image raw data, generate one or more first intermediate results corresponding to the one or more first sub tasks, and provide the one or more first intermediate results. The each second node may further include a reconstruction and display module in communication with the hardware resource management module of the each second node and the control and data transmission interface of the each second node. The control and data transmission interface of the each second node may be further configured to receive second image raw data. The reconstruction and display module may be configured to obtain, based on the second image raw data, second standardized processing capacities of the distributed system through the hardware resource management module of the each second node.

According to another aspect of the present disclosure, a system for operating an image reconstruction service platform to interact with service receiver nodes and service provider nodes is provided. The system may include a bus; a storage medium electronically connected to the bus and storing a set of instructions for image reconstruction; logic circuits electronically connected to the storage medium. When executing the set of instructions, the logic circuits may be directed to conduct logic operations including: obtaining image raw data; determining, based on a complexity of a reconstruction task of the image raw data and processing capacities of one or more workstations, from the one or more workstations, one or more hardware resources for processing the image raw data; generating, based on the image raw data and the one or more hardware resources, one or more sub tasks of the reconstruction task; assigning the one or more sub tasks to the one or more hardware resources; obtaining one or more intermediate results corresponding to the one or more sub tasks, the one or more intermediate results being generated by the one or more hardware resources; and generating, based on the one or more intermediate results, a reconstruction result of the image raw data.

According to another aspect of the present disclosure, a system for operating an image reconstruction service platform to interact with service receiver nodes and service provider nodes is provided. The system may include a bus; a storage medium electronically connected to the bus and storing a set of instructions for image reconstruction; logic circuits electronically connected to the storage medium. When executing the set of instructions, the logic circuits may be directed to conduct logic operations including: determining, according to a capacity quantization standard, processing capacities of local resources; providing the processing capacities for an image reconstruction service platform; receiving one or more sub tasks and image raw data relating to the one or more sub tasks from the image reconstruction service platform; generating one or more intermediate results by performing, based on the image raw data, the one or more sub tasks; and providing the one or more intermediate results for the image reconstruction service platform.

According to another aspect of the present disclosure, a system for operating an image reconstruction service platform to interact with service receiver nodes and service provider nodes is provided. The system may include a bus; a storage medium electronically connected to the bus and storing a set of instructions for image reconstruction; logic circuits electronically connected to the storage medium. When executing the set of instructions, the logic circuits may be directed to conduct logic operations including: obtaining, by a target second node of the plurality of second nodes, first standardized processing capacities of one or more nodes of the plurality of first nodes and/or the plurality of second nodes, based on first image raw data generated by an image scanning process, wherein the target second node has a service request for image reconstruction; generating, by the target second node, based on the first image raw data and the first standardized processing capacities, a first reconstruction task including one or more first sub tasks; assigning, by the target second node, the one or more first sub tasks to at least a portion of the one or more nodes; receiving, by the at least a portion of the one or more nodes, the one or more first sub tasks and the first image raw data; generating, by the at least a portion of the one or more nodes, one or more first intermediate results by performing, based on the first image raw data, the one or more first sub tasks; and providing, by the at least a portion of the one or more nodes, the one or more first intermediate results for the target second node.

According to one aspect of the present disclosure, a non-transitory computer readable medium is provided. The non-transitory computer readable medium stores instructions, the instructions, when executed by at least one processor, causing the at least one processor to implement a method comprising: obtaining image raw data; determining, based on a complexity of a reconstruction task of the image raw data and processing capacities of one or more workstations, from the one or more workstations, one or more hardware resources for processing the image raw data; generating, based on the image raw data and the one or more hardware resources, one or more sub tasks of the reconstruction task; assigning the one or more sub tasks to the one or more hardware resources; obtaining one or more intermediate results corresponding to the one or more sub tasks, the one or more intermediate results being generated by the one or more hardware resources; and generating, based on the one or more intermediate results, a reconstruction result of the image raw data.

According to one aspect of the present disclosure, a non-transitory computer readable medium is provided. The non-transitory computer readable medium stores instructions, the instructions, when executed by at least one processor, causing the at least one processor to implement a method comprising: determining, according to a capacity quantization standard, processing capacities of local resources; providing the processing capacities for an image reconstruction service platform; receiving one or more sub tasks and image raw data relating to the one or more sub tasks from the image reconstruction service platform; generating one or more intermediate results by performing, based on the image raw data, the one or more sub tasks; and providing the one or more intermediate results for the image reconstruction service platform.

According to one aspect of the present disclosure, a non-transitory computer readable medium is provided. The non-transitory computer readable medium stores instructions, the instructions, when executed by at least one processor, causing the at least one processor to implement a method comprising: obtaining, by a target second node of the plurality of second nodes, first standardized processing capacities of one or more nodes of the plurality of first nodes and/or the plurality of second nodes, based on first image raw data generated by an image scanning process, wherein the target second node has a service request for image reconstruction; generating, by the target second node, based on the first image raw data and the first standardized processing capacities, a first reconstruction task including one or more first sub tasks; assigning, by the target second node, the one or more first sub tasks to at least a portion of the one or more nodes; receiving, by the at least a portion of the one or more nodes, the one or more first sub tasks and the first image raw data; generating, by the at least a portion of the one or more nodes, one or more first intermediate results by performing, based on the first image raw data, the one or more first sub tasks; and providing, by the at least a portion of the one or more nodes, the one or more first intermediate results for the target second node.

Additional features will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following and the accompanying drawings or may be learned by production or operation of the examples. The features of the present disclosure may be realized and attained by practice or use of various aspects of the methodologies, instrumentalities and combinations set forth in the detailed examples discussed below.

In the following detailed description, numerous specific details are set forth by way of examples in order to provide a thorough understanding of the relevant disclosure. However, it should be apparent to those skilled in the art that the present disclosure may be practiced without such details. In other instances, well-known methods, procedures, systems, components, and/or circuitry have been described at a relatively high-level, without detail, in order to avoid unnecessarily obscuring aspects of the present disclosure. Various modifications to the disclosed embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the present disclosure. Thus, the present disclosure is not limited to the embodiments shown, but to be accorded the widest scope consistent with the claims.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments of the invention. As used herein, the singular forms “a,” “an,” and “the,” are intended to include the plural forms as well, unless the context clearly indicates otherwise. As used herein, the terms “and/or” and “at least one of” include any and all combinations of one or more of the associated listed items. It will be further understood that the terms “comprises,” “comprising,” “includes,” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Also, the term “exemplary” is intended to refer to an example or illustration.

It will be understood that, although the terms “first,” “second,” “third,” etc., may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of exemplary embodiments of the present disclosure.

These and other features, and characteristics of the present disclosure, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, may become more apparent upon consideration of the following description with reference to the accompanying drawings, all of which form a part of this disclosure. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended to limit the scope of the present disclosure. It is understood that the drawings are not to scale.

An aspect of the present disclosure relates to systems and methods for image reconstruction. According to some systems and methods of the present disclosure, a processing device may obtain image raw data; determine, based on a complexity of a reconstruction task of the image raw data and processing capacities of one or more workstations, from the one or more workstations, one or more hardware resources for processing the image raw data; generate, based on the image raw data and the one or more hardware resources, one or more sub tasks of the reconstruction task; assign the one or more sub tasks to the one or more hardware resources; obtain one or more intermediate results corresponding to the one or more sub tasks, the one or more intermediate results being generated by the one or more hardware resources; and generate, based on the one or more intermediate results, a reconstruction result of the image raw data.

According to the systems and methods of the present disclosure: (1) image raw data acquisition and data processing can be decoupled, a service receiver that has a need for image reconstruction can use hardware resources from a plurality of service providers, the service receiver may have no need to configure hardware resources of a workstation of its own according to a maximum processing capacity, thereby reducing the cost for configuring the workstation, and increasing the utilization rate of the hardware resources of service providers; (2) the processing capacities of workstations can be determined based on a capacity quantization standard, and hardware resources corresponding to different imaging devices can be modularized, thereby facilitating cooperation with different hardware resources, realizing hardware resource sharing between different workstations of imaging devices, and further increasing the utilization rate of the hardware resources; (3) a dynamic pricing mechanism can be used in the system, and service prices of service providers can be dynamically determined, thereby increasing the applicability and flexibility of the system; (4) the hardware resources of the service providers can be modularized or non-modularized, thereby increasing the universality, openness, extendibility of the system; (5) the system can provide a platform for sharing hardware resources, as well as a platform for sharing reconstructed images; (6) the system can provide a reconstruction and display module for displaying reconstructed images, thereby facilitating the system to determine individual scanning strategies that are adaptive to a displaying interface of the reconstruction and display module, and improving the display effect of the reconstructed images.

1 FIG. 100 100 110 120 130 140 100 140 110 140 110 120 110 130 110 130 120 130 140 130 140 120 is a schematic diagram illustrating an exemplary image reconstruction system according to some embodiments of the present disclosure. In some embodiments, the image reconstruction systemmay be configured to reconstruct one or more images. Exemplary image reconstruction process may include image generation (e.g., reconstructing one or more images based on image raw data), removing an image artifact, image registration, image fusion, image denoising, or the like, or a combination thereof. In some embodiments, the image reconstruction systemmay include one or more servers, a network, one or more terminal devices, and one or more storage devices. The components in the image reconstruction systemmay be connected in one or more of various ways. Merely by way of example, the storage devicemay be connected to the serverdirectly (as indicated by the bi-directional arrow in dotted lines linking the storage deviceand the server) or through the network. As another example, the servermay be connected to the terminal devicedirectly (as indicated by the bi-directional arrow in dotted lines linking the serverand the terminal device) or through the network. As still another example, the terminal devicemay be connected to the storage devicedirectly (as indicated by the bi-directional arrow in dotted lines linking the terminal deviceand the storage device) or through the network.

110 110 110 110 130 140 120 110 130 140 110 110 200 2 FIG. In some embodiments, the servermay be a single server or a server group. The server group may be centralized or distributed (e.g., the servermay be a distributed system). In some embodiments, the servermay be local or remote. For example, the servermay access information and/or data stored in the terminal deviceand/or the storage devicevia the network. As another example, the servermay be directly connected to the terminal deviceand/or the storage deviceto access stored information and/or data. In some embodiments, the servermay be implemented on a cloud platform or an onboard computer. Merely by way of example, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, or the like, or any combination thereof. In some embodiments, the servermay be implemented on a computing deviceincluding one or more components illustrated inin the present disclosure.

110 110 1 110 2 110 110 110 n In some embodiments, the serversmay include one or more servers such as a server-, a server-, . . . , a server-. The serversmay be configured to perform one or more functions for data acquisition, hardware resource determination, sub task generation, sub task assigning, image reconstruction, or the like, or any combination thereof, independently or jointly. Merely by way of example, the serversmay include one or more service receivers (or second nodes), one or more service providers (or first nodes), etc.

110 112 110 1 112 1 110 2 112 2 110 112 112 140 130 112 112 1 FIG. n n In some embodiments, the serversmay include one or more processing devices. As shown in, the server-may include a processing device-, the server-may include a processing device-, . . . , and the server-may include a processing device-. The processing devicesmay process information and/or data stored in the storage devices (e.g., the storage device) and/or generated (or provided) by the terminal deviceto perform one or more functions described in the present disclosure. In some embodiments, the processing devicesmay include one or more processing engines (e.g., single-core processing engine(s) or multi-core processor(s)). Merely by way of example, the processing devicesmay include a Central Processing Unit (CPU), an Application-Specific Integrated Circuit (ASIC), an Application-Specific Instruction-set Processor (ASIP), a Graphics Processing Unit (GPU), a Physics Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller unit, a Reduced Instruction-Set Computer (RISC), a microprocessor, or the like, or any combination thereof.

110 120 130 140 100 110 130 140 100 In some embodiments, at least a portion of the serversmay be connected to the networkto communicate with one or more components (e.g., the terminal device, the storage device) of the image reconstruction system. In some embodiments, at least a portion of the serversmay be directly connected to or communicate with one or more components (e.g., the terminal device, the storage device) of the image reconstruction system.

120 110 130 140 100 100 120 130 110 120 112 130 120 112 140 120 140 110 120 112 130 120 112 130 120 120 120 120 120 100 120 The networkmay facilitate exchange of information and/or data. In some embodiments, one or more components (e.g., the server, the terminal device, or the storage device) of the image reconstruction systemmay send information and/or data to other component(s) of the image reconstruction systemvia the network. For example, the terminal devicemay transmit image raw data to the servervia the network. As another example, the processing devicemay receive image raw data transmitted by the terminal devicevia the network. As still another example, the processing devicemay obtain image raw data from the storage devicevia the network. As still another example, the storage devicemay obtain reconstructed image(s) for storing from the servervia the network. As still another example, the processing devicemay receive a request for image reconstruction from the terminal devicevia the network. As still another example, the processing devicemay send one or more sub tasks, one or more intermediate results, and/or one or more reconstructed images to the terminal devicevia the network. In some embodiments, the networkmay be any type of wired or wireless network, or combination thereof. Merely by way of example, the networkmay include a cable network, a wireline network, an optical fiber network, a tele communications network, an intranet, an Internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Public Telephone Switched Network (PSTN), a Bluetooth network, a ZigBee network, a Near Field Communication (NFC) network, or the like, or any combination thereof. In some embodiments, the networkmay include one or more network access points. For example, the networkmay include wired or wireless network access points, through which one or more components of the image reconstruction systemmay be connected to the networkto exchange data and/or information.

130 130 1 130 2 130 3 130 4 130 5 130 1 130 4 130 5 110 130 In some embodiments, the terminal device(s)may include a mobile device-, a tablet computer-, a laptop computer-, a computing device-connected to an imaging device, a wearable device-, or the like, or any combination thereof. In some embodiments, the mobile device-may include a smart home device, a smart mobile device, a virtual reality device, an augmented reality device, or the like, or any combination thereof. In some embodiments, the smart home device may include a smart lighting device, a control device of an intelligent electrical apparatus, a smart monitoring device, a smart television, a smart video camera, an interphone, or the like, or any combination thereof. In some embodiments, the smart mobile device may include a smartphone, a Personal Digital Assistance (PDA), a gaming device, a navigation device, a Point of Sale (POS) device, or the like, or any combination thereof. In some embodiments, the virtual reality device and/or the augmented reality device may include a virtual reality helmet, virtual reality glasses, a virtual reality patch, an augmented reality helmet, augmented reality glasses, an augmented reality patch, or the like, or any combination thereof. For example, the virtual reality device and/or the augmented reality device may include Google™ Glasses, an Oculus Rift™, a HoloLens™, a Gear VR™, etc. In some embodiments, the computing device-may include a console computer connected to an imaging device. In some embodiments, the wearable device-may include a smart bracelet, a smart footgear, smart glasses, a smart helmet, a smart watch, smart clothing, a smart backpack, a smart accessory, or the like, or any combination thereof. In some embodiments, the servermay be integrated into the terminal device(s).

130 100 130 100 100 130 100 130 100 130 140 The terminal device(s)may be configured to facilitate communications between a user (e.g., an engineer, a doctor, a technician) and the image reconstruction system. For example, the user may send a data request via the terminal deviceto the image reconstruction system. As another example, the user may retrieve and/or download data and/or information stored in one or more storage devices of the image reconstruction systemvia the terminal device. As still another example, the user may retrieve or download data of interest by logging in an application coupled with (or that can communicate with) the image reconstruction systemvia the terminal device. In some embodiments, the application coupled with the image reconstruction systemmay facilitate the processing and/or management of data generated (or provided) by the terminal deviceand/or stored in the storage device. In some embodiments, the application may provide a data access interface for one or more users of the data, so that the user(s) may retrieve or download data of interest via the application for further use of the data.

140 140 130 140 140 140 110 110 140 110 140 110 The storage devicemay store data and/or instructions. In some embodiments, the storage devicemay store data obtained from the terminal device, such as a data request. In some embodiments, the storage devicemay store image raw data. For example, the storage devicemay store image raw data obtained from an imaging device. In some embodiments, the storage devicemay store data generated or processed by the server. For example, the servermay reconstruct, based on the image data, to obtain reconstructed image(s), and/or the storage devicemay store the reconstructed image(s). As another example, the servermay determine hardware resource for processing the image raw data, generate one or more sub tasks for image reconstruction, assign the one or more sub tasks to the hardware resources, obtain one or more intermediate results corresponding to the one or more sub tasks, and/or generating, based on the one or more intermediate results, a reconstruction result of the image raw data. In some embodiments, the storage devicemay store data and/or instructions that the servermay execute or use to perform exemplary methods described in the present disclosure.

140 120 110 130 100 100 140 120 140 110 130 100 140 110 140 130 140 100 100 In some embodiments, the storage devicemay be connected to the networkto communicate with one or more components (e.g., the server, terminal device) of the image reconstruction system. One or more components of the image reconstruction systemmay access the data or instructions stored in the storage devicevia the network. In some embodiments, the storage devicemay be directly connected to or communicate with one or more components (e.g., the server, the terminal device) of the image reconstruction system. In some embodiments, at least a portion of the storage devicemay be part of the server. In some embodiments, at least a portion of the storage devicemay be integrated in the terminal device. In some embodiments, at least a portion of the storage devicesmay be set in one or more IDCs. Merely for illustration, only one storage device is illustrated in the image reconstruction system. However, it should be noted that the image reconstruction systemin the present disclosure may also include multiple storage devices.

140 140 In some embodiments, the storage devicemay include a mass storage, a removable storage, a volatile read-and-write memory, a read-only memory (ROM), or the like, or any combination thereof. Exemplary mass storage may include a magnetic disk, an optical disk, a solid-state drive, etc. Exemplary removable storage may include a flash drive, a floppy disk, an optical disk, a memory card, a zip disk, a magnetic tape, etc. Exemplary volatile read-and-write memory may include a Random Access Memory (RAM). Exemplary RAM may include a Dynamic RAM (DRAM), a Double Date Rate Synchronous Dynamic RAM (DDR SDRAM), a Static RAM (SRAM), a Thyrisor RAM (T-RAM), and a Zero-capacitor RAM (Z-RAM), etc. Exemplary ROM may include a Mask ROM (MROM), a Programmable ROM (PROM), an Erasable Programmable ROM (EPROM), an ELectrically-Erasable Programmable ROM (EEPROM), a Compact Disk ROM (CD-ROM), and a digital versatile disk ROM, etc. In some embodiments, the storage devicemay be implemented on a cloud platform. Merely by way of example, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, or the like, or any combination thereof.

100 100 140 100 It should be noted that the image reconstruction systemis merely provided for the purposes of illustration, and is not intended to limit the scope of the present disclosure. For persons having ordinary skills in the art, multiple variations or modifications may be made under the teachings of the present disclosure. However, those variations and modifications do not depart from the scope of the present disclosure. For example, the image reconstruction systemmay be implemented on other devices to realize similar or different functions. As another example, the storage devicemay be omitted from the image reconstruction system.

2 FIG. 110 200 112 200 112 is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary computing device according to some embodiments of the present disclosure. In some embodiments, the servermay be implemented on the computing device. For example, the processing devicemay be implemented on the computing deviceand configured to perform functions of the processing devicedisclosed in this disclosure.

200 100 112 100 200 100 The computing devicemay be used to implement any component of the image reconstruction systemof the present disclosure. For example, the processing deviceof the image reconstruction systemmay be implemented on the computing device, via its hardware, software program, firmware, or a combination thereof. Although only one such computer is shown for convenience, the computer functions related to the image reconstruction systemas described herein may be implemented in a distributed manner on a number of similar platforms to distribute the processing load.

200 250 120 200 220 210 210 The computing device, for example, may include communication (COM) portsconnected to and from a network (e.g., the network) connected thereto to facilitate data communications. The computing devicemay also include a processor (e.g., a processor), in the form of one or more processors (e.g., logic circuits), for executing program instructions. For example, the processor may include interface circuits and processing circuits therein. The interface circuits may be configured to receive electronic signals from a bus, wherein the electronic signals encode structured data and/or instructions for the processing circuits to process. The processing circuits may conduct logic calculations, and then determine a conclusion, a result, and/or an instruction encoded as electronic signals. Then the interface circuits may send out the electronic signals from the processing circuits via the bus.

200 270 230 240 200 200 230 240 220 200 260 200 200 The computing devicemay further include program storage and data storage of different forms, for example, a disk, and a Read Only Memory (ROM), or a Random Access Memory (RAM), for storing various data files to be processed and/or transmitted by the computing device. The computing devicemay also include program instructions stored in the ROM, the RAM, and/or other type of non-transitory storage medium to be executed by the processor. The methods and/or processes of the present disclosure may be implemented as the program instructions. The computing devicemay also include an I/O component, supporting input/output between the computing deviceand other components therein. The computing devicemay also receive programming and data via network communications.

200 200 200 200 Merely for illustration, only one processor is described in the computing device. However, it should be noted that the computing devicein the present disclosure may also include multiple processors, and thus operations that are performed by one processor as described in the present disclosure may also be jointly or separately performed by the multiple processors. For example, the processor of the computing deviceexecutes both operation A and operation B. As in another example, operation A and operation B may also be performed by two different processors jointly or separately in the computing device(e.g., the first processor executes operation A and the second processor executes operation B, or the first and second processors jointly execute operations A and B).

3 FIG. 3 FIG. 300 310 320 330 340 1 350 360 390 300 370 380 360 390 340 380 112 350 112 100 120 is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary mobile device on which a terminal device may be implemented according to some embodiments of the present disclosure. As illustrated in, the mobile devicemay include a communication platform, a display, a Graphic Processing Unit (GPU), a Central Processing Unit (CPU), an/O, a memory, and storage. In some embodiments, any other suitable component, including but not limited to a system bus or a controller (not shown), may also be included in the mobile device. In some embodiments, a mobile operating system(e.g., iOS™, Android™, Windows Phone™) and one or more applicationsmay be loaded into the memoryfrom the storagein order to be executed by the CPU. The applicationsmay include a browser or any other suitable mobile apps for receiving and rendering information relating to image reconstruction or other information from the processing device. User interactions with the information stream may be achieved via the i/Oand provided to the processing deviceand/or other components of the image reconstruction systemvia the network.

To implement various modules, units, and their functionalities described in the present disclosure, computer hardware platforms may be used as the hardware platform(s) for one or more of the elements described herein. A computer with user interface elements may be used to implement a Personal Computer (PC) or any other type of work station or terminal device. A computer may also act as a server if appropriately programmed.

4 4 FIGS.A andB 1 FIG. 2 FIG. 3 FIG. 112 112 112 112 112 220 340 112 200 112 200 112 112 200 340 are block diagrams illustrating exemplary processing devices according to some embodiments of the present disclosure. In some embodiments, the processing deviceA and the processing deviceB may be embodiments of the processing deviceas described in connection with. In some embodiments, the processing deviceA and the processing deviceB may be respectively implemented on a processing unit (e.g., the processorillustrated in, or the CPUas illustrated in). Merely by way of example, the processing deviceA may be implemented on the computing deviceof a service receiver, and the processing deviceB may be implemented on the computing deviceof a service provider. Alternatively, the processing deviceA and the processing deviceB may be implemented on the same computing device, or the same CPU.

100 100 100 100 In some embodiments, the image reconstruction systemmay include an image reconstruction service platform, or the image reconstruction service platform may be coupled with the image reconstruction system. In some embodiments, the image reconstruction systemmay further include one or more service receiver nodes, and/or one or more service provider nodes. The image reconstruction service platform may be configured to perform functions relating to image reconstruction disclosed in this disclosure. For example, the image reconstruction service platform may obtain, from at least one of the one or more service receiver nodes, image raw data; determine, based on a complexity of a reconstruction task of the image raw data, processing capacities of the one or more service receiver nodes, and/or processing capacities of the one or more service provider nodes, from the one or more service receiver nodes and the one or more service provider nodes, one or more hardware resources for processing the image raw data; determine, based on the image raw data and the one or more hardware resources, one or more sub tasks of a reconstruction task; assign the one or more sub tasks to the one or more hardware resources; obtain one or more intermediate results corresponding to the one or more sub tasks, in which the one or more intermediate results may be generated by the one or more hardware resources; generate, based on the one or more intermediate results, a reconstruction result of the image raw data. In some embodiments, each of the one or more service provider nodes may be configured to: determine, according to a capacity quantization standard, processing capacities of resources of the each service provider node; provide the processing capacities for the image reconstruction service platform; receive at least one of the one or more sub tasks and at least a portion of the image raw data relating to the at least one sub task from the image reconstruction service platform; generate at least one intermediate result by performing, based on the at least a portion of the image raw data, the at least one sub task; and/or provide the at least one intermediate result for the image reconstruction service platform. In some embodiments, the image reconstruction systemmay be a distributed system. In some embodiments, the image reconstruction service platform may be implemented on the at least one of the one or more service receiver nodes. In some embodiments, the image reconstruction service platform may be implemented on the one or more service receiver nodes, and/or the one or more service provider nodes.

100 100 In some embodiments, the image reconstruction systemmay include one or more workstations. In some embodiments, a workstation may be or include or correspond to (or coupled to) a node (e.g., a service receiver node (or a second node), a service provider node (or a first node)) of the image reconstruction system. In some embodiments, the workstation may provide hardware resources for the node. In some embodiments, a service receiver (node) may refer to a node that has a need or request for an image reconstruction service. In some embodiments, a service provider (node) may refer to a node that has an ability to provide one or more image reconstruction services or a portion thereof. In some embodiments, the workstation of a service provider (node) may include hardware resources for implementing image reconstruction programs to provide an image reconstruction service (or a portion thereof). In some embodiments, an image reconstruction service (or a portion thereof) of a service receiver (node) may be provided or achieved by the service receiver (node) itself. In some embodiments, an image reconstruction service (or a portion thereof) of a service receiver (node) may be provided or achieved by one or more service providers (nodes). In some embodiments, a service receiver (node) of a first image reconstruction service may also be a service provider (node) of a second image reconstruction service. In some embodiments, a service provider (node) of a third image reconstruction service may also be a service receiver (node) of a fourth image reconstruction service. For example, the workstation of a service provider (node) may provide image reconstruction services for different service receivers (nodes) at the same time.

120 In some embodiments, the image reconstruction service platform may be configured as a cloud computing platform. For example, the service receiver(s) (or node(s)), and/or the service provider(s) (or node(s)) may not be directly coupled to the image reconstruction service platform. Alternatively, the service receiver(s) (or node(s)), and/or the service provider(s) (or node(s)) may be in communication with the image reconstruction service platform via the network. In some embodiments, a service receiver (node) may transmit image raw data to the cloud platform. In some embodiments, the cloud platform may receive the image raw data, and determine, based on a complexity of a reconstruction task of the image raw data and/or processing capacities of one or more workstations, from the one or more workstations, one or more hardware resources for processing the image raw data. In some embodiments, the cloud platform may generate, based on the image raw data and/or the one or more hardware resources, one or more sub tasks of the reconstruction task. In some embodiments, the cloud platform may transmit the sub task(s) to the service receiver (node). In some embodiments, the service receiver (node) may assign the sub task(s) to the hardware resources. In some embodiments, the cloud platform may directly assign the sub task(s) to the hardware resources. In some embodiments, the cloud platform may obtain one or more intermediate results corresponding to the one or more sub tasks and generate, based on the one or more intermediate results, a reconstruction result of the image raw data. In some embodiments, the cloud platform may transmit the reconstruction result to the service receiver (node). In some embodiments, the service receiver (node) may directly obtain the one or more intermediate results from one or more service provider(s) (or node(s)), and generate the reconstruction result.

112 112 112 112 112 112 112 112 112 112 112 112 112 In some embodiments, the processing deviceA may be implemented on the computing device of a service receiver (e.g., a second node). In some embodiments, the processing deviceA may be coupled to a service receiver (node). In some embodiments, the computing device of a service receiver may be connected to or in communication with an imaging device. In some embodiments, the processing deviceB may be implemented on the computing device of a service provider (e.g., a first node). In some embodiments, the processing deviceB may be coupled to a service provider (node). In some embodiments, a service provider (node) may be coupled to a processing deviceB. In some embodiments, a service provider (node) may be coupled to a processing deviceA, or a service receiver (node) may be coupled to a processing deviceB. In this case, a node may be coupled to the processing deviceA and the processing deviceB at the same time (i.e., the node may be a service receiver and a service provider at the same time). In some embodiments, the image reconstruction service platform may control the processing deviceA and/or the processing deviceB to perform the functions relating to image reconstruction. In some embodiments, the processing deviceA may be a portion of the image reconstruction service platform. In some embodiments, the processing deviceB may be a portion of the image reconstruction service platform.

112 110 402 404 406 408 410 The processing deviceA of the servermay include an obtaining module, a hardware resource determining module, a sub task generating module, an assigning module, and a reconstruction result generating module.

402 404 404 406 408 410 402 404 406 408 410 5 FIG. The obtaining modulemay obtain image raw data, and/or one or more intermediate results corresponding to one or more sub tasks. The hardware resource determining modulemay determine, based on a complexity of a reconstruction task of the image raw data and processing capacities of one or more workstations, from the one or more workstations, one or more hardware resources for processing the image raw data. In some embodiments, the hardware resource determining modulemay determine, based on an evaluation rule for matching the complexity of the reconstruction task of the image raw data and the processing capacities of one or more workstations, the one or more hardware resources. The sub task generating modulemay generate, based on the image raw data and the one or more hardware resources, one or more sub tasks of the reconstruction task. The assigning modulemay assign one or more sub tasks to one or more hardware resources. The reconstruction result generating modulemay generate, based on one or more intermediate results, a reconstruction result of the image raw data. More descriptions of the obtaining module, the hardware resource determining module, the sub task generating module, the assigning module, and the reconstruction result generating modulemay be found elsewhere in the present disclosure (e.g.,and the descriptions thereof).

112 110 412 414 416 418 The processing deviceB of the servermay include an obtaining module, a processing capacity determining module, an intermediate result generating module, and a transmitting module.

412 414 414 416 418 412 414 416 418 6 FIG. The obtaining modulemay obtain (e.g., receive) one or more sub tasks and/or image raw data relating to the one or more sub tasks from an image reconstruction service platform. The processing capacity determining modulemay determine, according to a capacity quantization standard, processing capacities of local resources. In some embodiments, the processing capacity determining modulemay determine the processing capacities of local resources based on a bearing capacity of each of the local resources for each of a plurality of types of image reconstruction units of a plurality of image reconstruction algorithms. The intermediate result generating modulemay generate one or more intermediate results by performing, based on the image raw data, the one or more sub tasks. The transmitting modulemay provide the processing capacities, the one or more intermediate results, a service price, and/or availabilities and/or accessibilities of local resources for the image reconstruction service platform. More descriptions of the obtaining module, the processing capacity determining module, the intermediate result generating module, and the transmitting modulemay be found elsewhere in the present disclosure (e.g.,and the descriptions thereof).

112 112 112 112 112 112 112 112 100 112 112 112 406 408 The modules in the processing deviceA and/or the processing deviceB may be connected to or communicate with each other via a wired connection or a wireless connection. The wired connection may include a metal cable, an optical cable, a hybrid cable, or the like, or any combination thereof. The wireless connection may include a Local Area Network (LAN), a Wide Area Network (WAN), a Bluetooth, a ZigBee, a Near Field Communication (NFC), or the like, or any combination thereof. In some embodiments, the processing deviceA and the processing deviceB may be combined as a single processing device. In some embodiments, the processing deviceA and/or the processing deviceB may include one or more additional modules. For example, the processing deviceA may also include a transmitting module (not shown) configured to transmit data and/or information (e.g., image raw data, sub task(s)) to one or more components (e.g., the processing deviceB) of the image reconstruction system. As another example, the processing deviceA and/or the processing deviceB may include a storage module (not shown) used to store information and/or data associated with image reconstruction. As a further example, the processing deviceB may include a service price determining module configured to determine a service price. In some embodiments, two or more of the modules may be combined into a single module, and any one of the modules may be divided into two or more units. For example, the sub task generating moduleand the assigning modulemay be combined as a single module.

5 FIG. 4 FIG.A 5 FIG. 500 100 500 140 270 390 112 220 200 340 300 500 500 500 is a flowchart illustrating an exemplary process for image reconstruction according to some embodiments of the present disclosure. In some embodiments, processmay be executed by the image reconstruction system. For example, the processmay be implemented as a set of instructions (e.g., an application) stored in a storage device (e.g., the storage device, the disk, and/or the storage). In some embodiments, the processing deviceA (e.g., the processorof the computing device, the CPUof the mobile device, and/or one or more modules illustrated in) may execute the set of instructions and may accordingly be directed to perform the process. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the processmay be accomplished with one or more additional operations not described and/or without one or more of the operations discussed. Additionally, the order of the operations of processillustrated inand described below is not intended to be limiting.

501 112 402 In, the processing deviceA (e.g., the obtaining module) may obtain image raw data.

As used herein, image raw data refers to raw data that is generated by an imaging device and is not processed. In some embodiments, the image raw data may be medical image raw data. For example, the image raw data may be associated with a specific portion (e.g., the head, the thorax, the abdomen), an organ (e.g., a lung, the liver, the heart, the stomach), and/or tissue (e.g., muscle tissue, connective tissue, epithelial tissue, nervous tissue) of a human or an animal.

In some embodiments, the imaging device (e.g., a first imaging device) that generates the image raw data may include a single modality imaging device. For example, the imaging device may include a Positron Emission Tomography (PET) device, a Single-Photon Emission Computed Tomography (SPECT) device, a Magnetic Resonance Imaging (MRI) device (also referred to as an MR device, or an MR scanner), a Computed Tomography (CT) device (e.g., a spiral CT, an electron beam CT, an energy spectrum CT), an Ultrasound (US) device, an X-ray imaging device, a Digital Subtraction Angiography (DSA) device, a Magnetic Resonance Angiography (MRA) device, a Computed Tomography Angiography (CTA) device, or the like, or any combination thereof. In some embodiments, the imaging device may include a multi-modality imaging device. Exemplary multi-modality imaging devices may include a PET-CT device, a PET-MRI device, a SPET-CT device, or the like, or any combination thereof. The multi-modality imaging device may perform multi-modality imaging simultaneously. For example, the PET-CT device may generate structural X-ray CT data and functional PET data simultaneously in a single scan. The PET-MRI device may generate MRI data and PET data simultaneously in a single scan.

112 130 140 100 120 112 260 200 250 350 300 310 In some embodiments, the imaging device may be coupled to (e.g., connected to) a service receiver (node). In some embodiments, the processing deviceA may obtain the image raw data from the imaging device that generates the image raw data or from the service receiver (node) coupled to the imaging device, one or more components (e.g., the terminal device, the storage device) of the image reconstruction system, or an external storage device via the network. In some embodiments, the processing deviceA may obtain the image raw data from the I/Oof the computing devicevia the communication port, and/or the I/Oof the mobile devicevia the communication platform.

503 112 404 In, the processing deviceA (e.g., the hardware resource determining module) may determine, based on a complexity of a reconstruction task of the image raw data and/or processing capacities of one or more workstations, from the one or more workstations, one or more hardware resources for processing the image raw data.

112 406 In some embodiments, the service receiver (node) coupled to the imaging device that generates the image raw data may have a service request or need for image reconstruction based on the image raw data. Exemplary image reconstruction may include image generation (e.g., reconstructing one or more images based on the image raw data), removing an image artifact, image registration, image fusion, image denoising, or the like, or a combination thereof. Accordingly, a reconstruction task may need to be performed. The reconstruction task may be generated by the processing deviceA (e.g., the sub task generating module). In some embodiments, the complexity of the reconstruction task of the image raw data may relate to an amount of the image raw data. For example, if the amount of the image raw data is relatively large, the complexity of the reconstruction task may be relatively high. As another example, if the amount of the image raw data is relatively small, the complexity of the reconstruction task may be relatively low.

100 In some embodiments, the image reconstruction systemmay include one or more workstations. Each workstation may include a plurality of hardware resources. In some embodiments, the hardware resources may be configured in a heterogeneous system architecture. In some embodiments, the hardware resources may include one or more Central Processing Units (CPU), one or more Graphics Processing Units (GPU), one or more Field Programmable Gate Arrays (FPGA), and/or one or more Application-Specific Integrated Circuits (ASIC), etc. In some embodiments, different workstations may have different hardware resources. For example, different workstations may have different amounts and/or types of hardware resources. In some embodiments, the hardware resources of a workstation may include one or more modularized hardware resources, and/or one or more non-modularized hardware resources. A modularized hardware resource may refer that the hardware resource is modularized. For example, a modularized CPU may refer that the CPU is configured as a plurality of modularized or standardized processing sub units or blocks. A non-modularized hardware resource may refer that the hardware resource is not modularized.

In some embodiments, hardware resources may include local hardware resources of the service receiver (node), remote hardware resources of one or more service providers (nodes). In some embodiments, a workstation that provides a remote hardware resource of a service provider (node) may be coupled to an imaging device with a same model number as the first imaging device, or an imaging device with a different model number from the first imaging device. In some embodiments, the hardware resources may be provided by or one or more manufacturers (e.g., a first manufacturer of the first imaging device, a second manufacturer different from the first manufacturer). In some embodiments, the hardware resources provided by the first manufacturer of the first imaging device may be modularized. In some embodiments, the hardware resources provided by the second manufacturer may be non-modularized. In some embodiments, different workstations (or hardware resources) may be owned by a same owner, or owned by different owners.

6 FIG. In some embodiments, each hardware resource may have a processing capacity. In some embodiments, each workstation may have a processing capacity, and the processing capacity of the each workstation may be determined based on the processing capacities of the hardware resources of the each workstation. In some embodiments, the processing capacity of a hardware resource may be standardized (also referred to as a standardized processing capacity), and accordingly, the processing capacity of a workstation may be standardized. More descriptions of the processing capacity may be found elsewhere in the present disclosure (e.g.,and descriptions thereof).

112 112 112 112 100 In some embodiments, the processing deviceA may determine, based on the complexity of the reconstruction task of the image raw data and/or processing capacities of one or more workstations, from the one or more workstations, one or more hardware resources for processing the image raw data. In some embodiments, the processing deviceA may determine, based on an evaluation rule for matching the complexity of the reconstruction task of the image raw data and the processing capacities of one or more workstations, the one or more hardware resources. In some embodiments, the processing deviceA may determine hardware resources whose processing capacities match the complexity of the reconstruction task of the image raw data according to a preset rule (also referred to as a matching rule). For example, the complexity of the reconstruction task may be quantified, and the preset rule may indicate a relationship between the complexity of the reconstruction task and processing capacities needed to realize the reconstruction task. The relationship may be denoted by a table, a function, a fitted curve, etc. In some embodiments, the processing deviceA may select, according to the matching rule, from one or more workstations of the image reconstruction system, hardware resources whose processing capacities are sufficient to accomplish the reconstruction task. In some embodiments, a summation of processing capacities of the selected hardware resources (or a portion thereof) may match the complexity of the reconstruction task of the image raw data.

112 112 112 112 In some embodiments, the processing deviceA may determine processing capacities of local resources (e.g., local hardware resources) of the service receiver (node). In some embodiments, the processing deviceA may determine whether the processing capacities of local resources match the complexity of the reconstruction task. In some embodiments, in response to a determination that the processing capacities of local resources match the complexity of the reconstruction task, the processing deviceA may designate at least a portion of the local resources as the hardware resources for processing the image raw data. In some embodiments, in response to a determination that the processing capacities of local resources mismatch the complexity of the reconstruction task, the processing deviceA may designate the local resources as a portion of the hardware resources for processing the image raw data. In some embodiments, if the processing capacities of local resources is surplus to the required processing capacity of the reconstruction task, the local resources (or a portion thereof) may be further used to provide another reconstruction service (or a portion thereof) (i.e., the service receiver (node) may be used as a service provider (node) of another reconstruction service (or a portion thereof)).

112 112 112 112 112 112 112 112 In some embodiments, the processing deviceA may determine processing capacities of a plurality of remote resources. For example, the processing deviceA may obtain the processing capacities of the plurality of remote resources from one or more processing devicesB. In some embodiments, the processing deviceA may determine, based on the processing capacities of the plurality of remote resources, one or more candidate remote resources (of one or more service providers (nodes)) whose processing capacities match the complexity of the reconstruction task. In some embodiments, the processing deviceA may determine, from one or more candidate remote resources, one or more target remote resources based on availabilities of the one or more candidate remote resources, accessibilities of the one or more candidate remote resources, processing capacities of the one or more candidate remote resources, network resource states (over the image reconstruction service platform), or service prices of the one or more candidate remote resources. In some embodiments, the processing deviceA may designate at least a portion of the one or more target remote resources as the one or more hardware resources for processing the image raw data. In some embodiments, the network resource states may include a type of network, transmission bandwidths between the one or more candidate remote resources and a computing device of the service receiver (node), and/or transmission delays from the one or more candidate remote resources to the computing device of the service receiver (node). In some embodiments, the network resource states (e.g., the transmission bandwidths and/or transmission delays) may be determined through a handshaking mechanism. In some embodiments, the processing deviceA may determine processing capacities of a plurality of remote resources; determine, based on the processing capacities of the plurality of remote resources, one or more target remote resources whose processing capacities match the complexity of the reconstruction task; and designate at least a portion of the one or more target remote resources as the one or more hardware resources for processing the image raw data. In some embodiments, the processing deviceA may determine, from the plurality of remote resources, the one or more target remote resources based on availabilities of the plurality of remote resources, accessibilities of the plurality of remote resources, processing capacities of the plurality of remote resources, network resource states, and/or service prices of the plurality of remote resources. In some embodiments, the network resource states (over the image reconstruction service platform) may include a type of the network, transmission bandwidths between one or more remote resources and a computing device of the service receiver (node), and/or transmission delays from one or more remote resources to the computing device of the service receiver (node).

112 112 112 In some embodiments, the processing deviceA may give priority to the use of local resources. For example, if the local resources (or a portion thereof) are available, the processing deviceA may designate the local resources (or a portion thereof) as at least a portion of the hardware resources. If the processing capacities of the local resources mismatch the complexity of the reconstruction task (e.g., the processing capacities of the local resources are not sufficient to accomplish the reconstruction task), the processing deviceA may designate remote resources as another portion of the hardware resources. In this case, both the local resources and the remote resources may be used to accomplish the reconstruction task.

112 112 In some embodiments, the processing deviceA may determine the hardware resources (e.g., local resources, remote resources) according to a dynamic price of each hardware resource, a current network resource state (e.g., a transmission bandwidth, a transmission delay), the availability and/or accessibility of the each hardware resource, and/or the processing capacity of the each hardware resource. For example, if a service price of the local resources is higher than a service price of remote resources, the processing deviceA may designate the remote resources whose service price is lower than the local resources as the hardware resources for accomplishing the reconstruction task. In this case, the local resources may be sold for other reconstruction service(s) to save cost.

505 112 406 In, the processing deviceA (e.g., the sub task generating module) may generate, based on the image raw data and/or the one or more hardware resources, one or more sub tasks of the reconstruction task.

112 112 112 In some embodiments, the processing deviceA may generate the sub task(s) according to a predetermined sub task generation algorithm, a preset sub task generation rule, etc. In some embodiments, each sub task may include image raw data that is used in the sub task. For example, the processing deviceA may determine an image reconstruction program used to implement the reconstruction task; parse or analyze the image reconstruction program; generate a plurality of image reconstruction program units (e.g., a first unit for filtered back projection, a second unit for iterative processing, etc.) based on the image reconstruction program; and generate the sub task(s) based on the plurality of image reconstruction program units (e.g., packaging one or more of the plurality of image reconstruction program units into a sub task). As another example, the processing deviceA may divide the image raw data into one or more portions; determine an image reconstruction program used to process each portion of the image raw data; and generate a sub task based on the image reconstruction program for each portion of the image raw data.

112 112 In some embodiments, the processing deviceA may determine a number (or count) of the sub tasks and/or a task load of each of the sub tasks, based on the amount of the image raw data, a number (or count) of the hardware resources, a processing capacity of each of the hardware resources. In some embodiments, the processing deviceA may generate the sub tasks based on the image raw data, the number (or count) of the sub tasks, and/or the task load of each of the sub tasks.

112 112 112 112 In some embodiments, the processing deviceA may determine the number (or count) of the sub tasks based on the image raw data (e.g., the amount of the image raw data). In some embodiments, the processing deviceA may determine the number (or count) of the hardware resources based on the number (or count) of the sub tasks. In some embodiments, the processing deviceA may determine the processing capacities of the hardware resources based on the task loads of the sub tasks. For example, each sub task may correspond to a hardware resource, and/or the processing capacity of each hardware resource may match the task load of a corresponding sub task. For instance, the processing deviceA may generate three sub tasks (e.g., sub task A, sub task B, sub task C) based on the image raw data, and may then determine the hardware resources whose processing capacitates match the task loads of the three sub tasks.

507 112 408 In, the processing deviceA (e.g., the assigning module) may assign the one or more sub tasks to the one or more hardware resources.

112 112 112 112 In some embodiments, the processing deviceA may assign the one or more sub tasks to the one or more hardware resources, based on the processing capacity of each hardware resource, the number (or count) of the sub tasks, task loads of the sub tasks, priorities of the sub tasks, and/or any other requirement relating to the sub tasks (e.g., a delivery deadline of each sub task, a budget of each sub task, etc.). In some embodiments, the processing deviceA may determine at least one hardware resource of the one or more hardware resources for implementing each sub task. In some embodiments, the processing capacities of the at least one hardware resource may match the each sub task. For example, if the processing capacities of hardware resources of three workstations (e.g., workstation D, workstation E, workstation F) match three sub tasks (e.g., sub task A, sub task B, sub task C), respectively, then the processing deviceA may assign sub task A to the hardware resource(s) of workstation D, assign sub task B to the hardware resource(s) of workstation E, and assign sub task C to the hardware resource(s) of workstation F. In some embodiments, the processing deviceA may transmit the one or more sub tasks and image raw data relating to the sub tasks to the corresponding hardware resource(s) (or to the corresponding service provider (node(s)) coupled to the corresponding hardware resource(s)), so that the corresponding hardware resource(s) may implement the sub task(s) and generate intermediate result(s) of the sub task(s).

112 112 112 In some embodiments, the processing deviceA may assign, according to an allocation strategy, the one or more sub tasks to the one or more hardware resources. In some embodiments, the allocation strategy may include a minimum processing time strategy, a minimum service price strategy, or a strategy preset by a user. For example, if the processing capacity of hardware resources of each of the three workstations (e.g., workstation D, workstation E, workstation F) match each of the three sub tasks (e.g., sub task A, sub task B, sub task C), the processing deviceA may determine a plurality of assigning plans (e.g., assigning sub tasks A/B/C to workstations D/E/F (or D/F/E, or E/D/F, or E/F/D, or F/D/E, or F/E/D) in sequence) and prices corresponding to the plurality of assigning plans, select an assigning plan that has a lowest price, and assign the sub tasks according to the selected assigning plan. As another example, if the processing capacity of hardware resources of each of the three workstations (e.g., workstation D, workstation E, workstation F) match each of the three sub tasks (e.g., sub task A, sub task B, sub task C), the processing deviceA may determine a plurality of assigning plans (e.g., assigning sub tasks A/B/C to workstations D/E/F (or D/F/E, or E/D/F, or E/F/D, or F/D/E, or F/E/D) in sequence) and a total processing time of the sub tasks corresponding to each of the assigning plans, select an assigning plan that has a minimum processing time, and assign the sub tasks according to the selected assigning plan.

509 112 402 In, the processing deviceA (e.g., the obtaining module) may obtain one or more intermediate results corresponding to the one or more sub tasks. The one or more intermediate results may be generated by the one or more hardware resources.

112 112 In some embodiments, after the processing deviceA transmits the one or more sub tasks and image raw data relating to the sub tasks to the corresponding hardware resource(s) (or to the corresponding service provider (node(s)) coupled to the corresponding hardware resource(s)), the corresponding hardware resource(s) may implement the sub task(s) and generate intermediate result(s) of the sub task(s). In some embodiments, the corresponding hardware resource(s) (or the corresponding service provider (node(s)) coupled to the corresponding hardware resource(s)) may transmit the intermediate result(s) to the processing deviceA.

511 112 410 In, the processing deviceA (e.g., the reconstruction result generating module) may generate, based on the one or more intermediate results, a reconstruction result of the image raw data.

112 In some embodiments, each of the one or more intermediate results may be a portion of the reconstruction result. In some embodiments, the processing deviceA may generate the reconstruction result by combining the one or more intermediate results. For example, an intermediate result may correspond to a processing result of a portion of the image raw data, and the one or more intermediate results may be combined to obtain the reconstruction result of the image raw data. Exemplary combining operations may include splicing, fusing, etc. In some embodiments, at least a portion of the intermediate results may be further processed, and the processed result(s) and/or the intermediate result(s) may be combined to obtain the reconstruction result. In some embodiments, the reconstruction result may be generated based on one or more reconstruction algorithms, and the intermediate results may be intermediate results used in the reconstruction algorithms. Exemplary reconstruction algorithms may include an analytic reconstruction algorithm, an iterative reconstruction algorithm, a Fourier-based reconstruction algorithm, or the like, or any combination thereof. Exemplary analytic reconstruction algorithms may include a Filter Back Projection (FBP) algorithm, a Back-Projection Filter (BPF) algorithm, or the like, or any combination thereof. Exemplary iterative reconstruction algorithms may include a Maximum Likelihood Expectation Maximization (ML-EM), an Ordered Subset Expectation Maximization (OSEM), a Row-Action Maximum Likelihood Algorithm (RAMLA), a Dynamic Row-Action Maximum Likelihood Algorithm (DRAMA), or the like, or any combination thereof. Exemplary Fourier-based reconstruction algorithm may include a classical direct Fourier algorithm, a Non-Uniform Fast Fourier Transform (NUFFT) algorithm, or the like, or any combination thereof.

500 503 505 It should be noted that the above description is merely provided for the purpose of illustration, and not intended to limit the scope of the present disclosure. For persons having ordinary skills in the art, multiple variations and modifications may be made under the teachings of the present disclosure. However, those variations and modifications do not depart from the scope of the present disclosure. In some embodiments, one or more operations may be added elsewhere in the process. For example, operationsandmay be integrated into a single operation.

6 FIG. 4 FIG.B 6 FIG. 600 100 600 140 270 390 112 220 200 340 300 600 600 600 is a flowchart illustrating an exemplary process for image reconstruction according to some embodiments of the present disclosure. In some embodiments, processmay be executed by the image reconstruction system. For example, the processmay be implemented as a set of instructions (e.g., an application) stored in a storage device (e.g., the storage device, the disk, and/or the storage). In some embodiments, the processing deviceB (e.g., the processorof the computing device, the CPUof the mobile device, and/or one or more modules illustrated in) may execute the set of instructions and may accordingly be directed to perform the process. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the processmay be accomplished with one or more additional operations not described and/or without one or more of the operations discussed. Additionally, the order of the operations of processillustrated inand described below is not intended to be limiting.

601 112 414 In, the processing deviceB (e.g., the processing capacity determining module) may determine, according to a capacity quantization standard, processing capacities of local resources.

112 100 112 112 112 In some embodiments, the local resources may refer to the hardware resources of a workstation that is coupled to a service provider (node) (the processing deviceB may be implemented on the computing device of the service provider (node)). In some embodiments, the image reconstruction systemmay include a plurality of service providers (or nodes), and the processing deviceB coupled to each service provider (node) may determine, the processing capacities of local resources of the workstation of the each service provider (node). In some embodiments, the processing deviceB may determine the processing capacities of local resources in response to a service request of the processing deviceA.

112 100 130 In some embodiments, the processing deviceB may determine, according to the capacity quantization standard, the processing capacities of local resources. The capacity quantization standard may refer to a criterion or rule for quantifying the processing capacities of local resources. In some embodiments, the capacity quantization standard may be determined by the image reconstruction system, or may be preset by a user or operator via the terminal(s).

112 In some embodiments, the processing deviceB may determine the processing capacities of local resources based on a bearing capacity of each of the local resources for each of a plurality of types of image reconstruction units of a plurality of image reconstruction algorithms. In some embodiments, a plurality of image reconstruction algorithms or programs that may be used in image reconstruction task may be predetermined. In some embodiments, the plurality of image reconstruction algorithms or programs may be divided into a plurality of types of image reconstruction units. In some embodiments, the plurality of types of image reconstruction units may be standardized according to a predetermined standard. For example, a first type of image reconstruction unit may be configured for image addition, a second type of image reconstruction unit may be configured for matrix multiplication, a third type of image reconstruction unit may be configured for image subtraction, etc. In some embodiments, each local resource may have a bearing capacity for each type of image reconstruction unit (e.g., the local resource may have the bearing capacity to implement certain number (or count) of image reconstruction units). In some embodiments, the processing capacity of each local resource may be determined based on the bearing capacities of the each local resource for the plurality of types of image reconstruction units. For example, the processing capacity of each local resource may be determined as a summation (e.g., a weighted sum) of the bearing capacities of the each local resource for the plurality of types of image reconstruction units. For instance, if the bearing capacity of a local resource for a first type of image reconstruction unit is 100 (i.e., 100 units in a unit time period), the bearing capacity of the local resource for a second type of image reconstruction unit is 150 (i.e., 150 units in a unit time period), the bearing capacity of the local resource for a third type of image reconstruction unit is 200 (i.e., 200 units in a unit time period), then the processing capacity of the local resource may be determined as 450 units (or a weighted sum thereof). In some embodiments, the bearing capacities of each local resource for the plurality of types of image reconstruction units may be normalized before summation.

603 112 418 In, the processing deviceB (e.g., the transmitting module) may provide the processing capacities for an image reconstruction service platform.

112 601 112 112 In some embodiments, the processing deviceB may transmit the processing capacities of local resources determined into the image reconstruction service platform (or a service receiver (node) on which the image reconstruction service platform is implemented). In some embodiments, the processing deviceA may determine processing capacities of local resources (i.e., the hardware resources of a workstation that is coupled to a service receiver (node), on whose computing device the processing deviceA is implemented) and provide the processing capacities for the image reconstruction service platform.

605 112 412 In, the processing deviceB (e.g., the obtaining module) may receive one or more sub tasks and image raw data relating to the one or more sub tasks from the image reconstruction service platform.

112 112 112 In some embodiments, if one or more sub tasks are assigned to the processing deviceB (or the service provider (node) to which the processing deviceB is coupled), the processing deviceB may receive the sub tasks and image raw data relating to the sub tasks from the image reconstruction service platform.

607 112 416 In, the processing deviceB (e.g., the intermediate result generating module) may generate one or more intermediate results by performing, based on the image raw data, the one or more sub tasks.

112 112 112 112 112 In some embodiments, after the processing deviceB (or the service provider (node) to which the processing deviceB is coupled) receives the sub tasks and image raw data relating to the sub tasks from the image reconstruction service platform, the processing deviceB may perform the sub tasks based on the received image raw data, and generate corresponding intermediate results. In some embodiments, each sub task may correspond to an intermediate result. In some embodiments, if two or more processing devicesB receives sub tasks and corresponding image raw data, the two or more processing devicesB may generate corresponding intermediate results.

609 112 418 In, the processing deviceB (e.g., the transmitting module) may provide the one or more intermediate results for the image reconstruction service platform.

112 112 112 112 In some embodiments, the processing deviceB (or the service provider (node) to which the processing deviceB is coupled) may transmit the one or more intermediate results to the image reconstruction service platform (or the service receiver (node) on which the image reconstruction service platform is implemented). In some embodiments, if two or more processing devicesB generate corresponding intermediate results, each of the two or more processing devicesB may transmit corresponding intermediate result(s) to the image reconstruction service platform (or the service receiver (node) on which the image reconstruction service platform is implemented).

600 601 603 112 112 112 112 It should be noted that the above description is merely provided for the purpose of illustration, and not intended to limit the scope of the present disclosure. For persons having ordinary skills in the art, multiple variations and modifications may be made under the teachings of the present disclosure. However, those variations and modifications do not depart from the scope of the present disclosure. In some embodiments, one or more operations may be added elsewhere in the process. For example, operationsandmay be integrated into a single operation. In some embodiments, the processing deviceB may determine a service price based on the processing capacities, and/or network resource states over the image reconstruction service platform. In some embodiments, the network resource states may include a type of the network, transmission bandwidths between the local resources and the image reconstruction service platform, and/or transmission delays from the local resources to the image reconstruction service platform, etc. In some embodiments, the processing deviceB may determine the service price dynamically. In some embodiments, the processing deviceB may provide the service price for the image reconstruction service platform (e.g., transmit the service price to the image reconstruction service platform). In some embodiments, the processing deviceB may provide availabilities and/or accessibilities of the local resources for the image reconstruction service platform (e.g., transmit the availabilities and/or accessibilities to the image reconstruction service platform).

7 FIG. 700 is a block diagram illustrating an exemplary image reconstruction apparatus according to some embodiments of the present disclosure. The image reconstruction apparatusmay be used for a distributed system that is configured for image reconstruction and sharing processing capacities.

7 FIG. 700 7110 7120 7120 7121 7122 7121 7122 As shown in, the image reconstruction apparatusmay include a computing device(also referred to as a console computer) and a service platform(also referred to as an image reconstruction service platform) implemented on the console computer. In some embodiments, the service platformmay include a hardware resource management module, and a control and data transmission interface. In some embodiments, the hardware resource management modulemay be configured to determine (e.g., calculate), according to a capacity quantization standard, first processing capacities (also referred to as first standardized processing capacities) of hardware resources (at its disposal) available for the service platform, and/or feed back (or provide) the first processing capacities to the service platform. In some embodiments, the control and data transmission interfacemay be configured to receive one or more first sub tasks (also referred to as reconstruction sub tasks) and first image raw data, generate one or more first intermediate results corresponding to the one or more first sub tasks, and provide the one or more first intermediate results for the service platform. In some embodiments, the first sub task(s) may include a reconstruction sub-task allocated or assigned by a second node of the distributed system, and the first image raw data may include image raw data transmitted from the second node. In some embodiments, the distributed system may include a first node that may act as a slave node in the distributed system. In some embodiments, the first node may only share processing capacities, and may not include a reconstruction and display module. In some embodiments, the processing capacities may be provided by a manufacturer of an imaging device that generates the first image raw data. The hardware resources may include hardware resources that the first node can acquire directly, third-party cloud hardware resources obtained via network communication, or the like, or a combination thereof. The use of the image reconstruction apparatus(es) disclosed in the present disclosure can decouple front-end data acquisition and transmission and back-end data processing into two separate parts, and a second node that has a need or request for image reconstruction can obtain processing capacities from one or more first nodes and/or one or more second nodes (e.g., second node 1, second node 2, . . . , second node N, etc.) of the distributed system for sharing processing capacities, without configuring or preparing the hardware resources of a workstation according to a peak processing capacity that may be used, thereby reducing the configuration cost of the workstation and improve the utilization rate of the hardware resources at the disposal of the first node. In some embodiments, the workstation may be coupled to (e.g., connected to) the imaging device.

700 7121 7122 7121 7122 In some embodiments, the image reconstruction apparatusmay further include a first workstation that includes hardware resources. In some embodiments, the first workstation may communicate with the hardware resource management moduleand the control and data transmission interface, respectively. The first workstation may be configured to feed back (or provide) the first processing capacities to the hardware resource management moduleaccording to the capacity quantization standard. The first workstation may be further configured to receive, from the control and data transmission interface, the first sub task(s) and the first image raw data, perform the first sub task(s) according to the first image raw data, and/or feed back (or provide) intermediate result(s) of the sub task(s).

7121 7121 7121 In some embodiments, the hardware resource management modulemay be configured to determine, based on the hardware resources at its disposal, the first processing capacities according to the capacity quantization standard, and/or feed back (or provide) the first processing capacities. Specifically, in some embodiments, the hardware resource management modulemay feed back (or provide) qualities and/or availabilities of the hardware resources to the second node that is in communication the hardware resource management module, according to the first processing capacities fed back by the first workstation and/or a network resource status (and/or quotation).

7122 7122 In some embodiments, the control and data transmission interfacemay obtain and feed back (or provide) the first intermediate result(s). Specifically, in some embodiments, the control and data transmission interfacemay receive the first intermediate result(s) fed back by the first workstation and feed back (or provide) the first intermediate result(s) to the second node.

7130 7130 7130 In some embodiments, the first workstation may include a plurality of standardized hardware resourcesconfigured in a heterogeneous system architecture. In some embodiments, the capacity quantization standard may include normalizing the processing capacity of each of the plurality of standardized hardware resourcesbased on the processing capacity characteristics of the standardized hardware resources. In some embodiments, the standardized hardware resources may include, but is not limited to, one or more CPUs, one or more GPUs, one or more FPGAs, and/or one or more ASICs.

Thus, the image reconstruction apparatus in the present disclosure can realize the modularization and normalization of data processing performed on image raw data generated by different types of imaging devices, thereby facilitating mutual cooperation. For example, CT and MRI may use standardized workstations. The processing capacities of acceleration hardware resources (such as CPU, GPU, FPGA, and ASIC) in the workstation(s) may be quantified, and the hardware resources of these standardized workstations can be probed or accessed by the second node(s) of the distributed system. In this way, it is possible to realize the sharing of the processing capacities of imaging devices of the same type and/or brand, imaging devices of different types but the same brand, imaging devices of the same type but different brands, and imaging devices of different types and different brands. As a result, users who purchase various types of imaging device(s), manufacturer(s) who sell the imaging device(s), and different users who use the same brand of imaging device can cooperate with each other via a network (e.g., a high-speed 5G network) to achieve processing capacity sharing and improve the utilization rate of resources.

8 FIG. 8 FIG. 7 FIG. 800 700 800 800 8110 7110 8120 7120 8120 8121 7121 8122 7122 800 8140 8121 8122 8122 8140 8121 8140 8122 8140 8140 is a block diagram illustrating an exemplary image reconstruction apparatus according to some embodiments of the present disclosure. As shown in, the image reconstruction apparatusmay be different from the image reconstruction apparatusillustrated in. In some embodiments, the image reconstruction apparatusmay be configured as a second node of the distributed system. In some embodiments, the second node may function as a master node. In some embodiments, the second node may obtain the processing capacities of hardware resources from the distributed system. In some embodiments, the image reconstruction apparatusmay include a computing device(also referred to as a console computer) (similar to the computing device) and a service platform(also referred to as an image reconstruction service platform) (similar to the service platform) implemented on the console computer. In some embodiments, the service platformmay include a hardware resource management modulesimilar to the hardware resource management module, and a control and data transmission interfacesimilar to the control and data transmission interface. In some embodiments, the image reconstruction apparatusmay further include a reconstruction and display modulethat is coupled to (e.g., connected to) or in communication with the hardware resource management moduleand the control and data transmission interface, respectively. The control and data transmission interfacemay be configured to receive image raw data (e.g., second image raw data). The reconstruction and display modulemay be configured to determine or obtain, based on a scan task that generates the image raw data and the image raw data, processing capacities (e.g., second processing capacities) (also referred to as second standardized processing capacities) of the distributed system through the hardware resource management module. In some embodiments, the reconstruction and display modulemay be configured to generate, according to the second processing capacities and/or the second image raw data, one or more second sub tasks of a second reconstruction task; and/or assign the second sub task(s) to the distributed system. In some embodiments, the control and data transmission interfacemay be further configured to receive one or more second intermediate results fed back by the distributed system and send the second intermediate result(s) to the reconstruction and display module. In some embodiments, the reconstruction and display modulemay be further configured to generate, based on the second intermediate result(s), a second reconstruction result; and/or display the second reconstruction result on a display device of the console computer. It is understood that the display device may include, but is not limited to a processor, a displayer, an audio and video playback device, etc.

800 700 800 700 In some embodiments, the image reconstruction apparatusmay be similar to at least a portion of the image reconstruction apparatus. In some embodiments, a portion of the image reconstruction apparatusmay be similar to the image reconstruction apparatus.

8121 8140 8140 8121 8140 In some embodiments, the hardware resource management modulemay be further configured to obtain a transmission delay, a transmission bandwidth, and/or a processing capacity quotation (or price) relating to the second processing capacities. In some embodiments, the reconstruction and display modulemay be further configured to determine an image reconstruction scheme (or a reconstruction task). Specifically, in some embodiments, the reconstruction and display modulemay generate the image reconstruction scheme according to a priority strategy of the second processing capacities, the transmission delay(s), the transmission bandwidth(s), and/or the second processing capacities provided by the hardware resource management module. In some embodiments, the reconstruction and display modulemay be configured to decompose the image reconstruction scheme into one or more second sub tasks. In some embodiments, a priority strategy of processing capacities may include a time priority, a cost priority, and/or an effect priority. In some embodiments, according to the characteristics of the processing capacities of each of the first nodes (e.g., slave nodes) (e.g., first node 1, first node 2, . . . , first node M, etc.), the image reconstruction scheme may be decomposed into one or more second sub tasks. In some embodiments, the time priority, the cost priority, or the effect priority may be designated as a preset priority strategy according to the actual working conditions.

8121 800 800 In some embodiments, the hardware resource management moduleof the image reconstruction apparatusmay be configured to feed back (or provide) the qualities and availabilities of hardware resources to a second node (or first node) that is in communication with it, based on the processing capacities (e.g., the first processing capacities, the second processing capacities) and network resource status (and/or quotation) fed back by a workstation (e.g., the first workstation, a second workstation not shown). Thus, the image reconstruction apparatusmay enable the manufacturer(s) of imaging device(s) to provide reconstruction processing capacities and share the processing capacities with customers through network communication (e.g., 5G network). In some embodiments, when purchasing imaging device(s), the customer(s) do not need to configure workstation(s) according to a peak processing capacity that may be used, but only need to configure workstation(s) according to the commonly used processing capacities, and purchase a small amount of processing capacities when needed. That is, it is possible to expand the types of different configurations of imaging devices that manufacturer(s) can provide. Moreover, it is possible to improve the economic benefits by sharing the reconstruction processing capacities with other customer(s) with paid. At the same time, the cost of purchasing imaging device(s) may be saved, and the utilization rate of hardware resources may be improved. Further, for the processing capacities sharing between a customer's own multiple imaging device(s) or workstation(s), and/or purchasing processing capacities from manufacturer(s) of imaging device(s) or workstation(s), the reconstruction efficiency can be improved for all imaging device(s) or workstation(s) in the distributed system over the service platform through reasonable distribution of the reconstruction sub tasks. Further, because the service platform can obtain the hardware resource configuration of the respective workstation(s), appropriate hardware resource(s) with appropriate processing capacities can be selected to better complete the reconstruction task according to different requirements of reconstruction tasks (e.g., transmission delay, transmission bandwidth, etc.). Furthermore, each of the second nodes of the distributed system may test the network bandwidth and/or delay through a handshake mechanism, and finds suitable hardware resources with suitable processing capacities. In some embodiments, the service platform may implement a dynamic bidding mechanism of processing capacities based on the qualities and/or availabilities of the processing capacities, network status(es), to provide dynamic pricing, thereby improving the applicability and flexibility of the distributed system which shares processing capacities of image reconstruction.

9 FIG. 9 FIG. 900 800 900 9121 9122 9121 9122 900 800 900 800 700 is a block diagram illustrating an exemplary image reconstruction apparatus according to some embodiments of the present disclosure. As shown in, the image reconstruction apparatusmay be different from the image reconstruction apparatusin that the image reconstruction apparatusmay further include a second workstation. In some embodiments, the second workstation may be coupled to (e.g., connected to) or in communication with the hardware resource management moduleand the control and data transmission interface, respectively. In some embodiments, the second workstation may be configured to feed back (or provide) third processing capacities (also referred to as third standardized processing capacities) to the hardware resource management moduleaccording to the capacity quantization standard. In some embodiments, the second workstation may be further configured to receive third sub task(s) and third image raw data from the control and data transmission interface; and/or perform the third sub task(s) and feed back (or provide) a third reconstruction result generated based on the third image raw data. The differences between the image reconstruction apparatusand the image reconstruction apparatusmay be described below. In the distributed system, any of the image reconstruction apparatuses having the second workstation and/or the reconstruction and display module (e.g., the image reconstruction apparatus) may function as a master node when it has a local reconstruction task and needs to obtain processing capacities from the distributed system; when it provides processing capacities for another image reconstruction apparatus (e.g., the image reconstruction apparatus, the image reconstruction apparatus) in the distributed system that needs to complete a reconstruction task, it may function as a slave node.

9121 7121 9121 9121 9140 9140 9122 9122 In some embodiments, in order to obtain the second processing capacities, the hardware resource management moduleof the second node (as a master node) may obtain fourth processing capacities from the hardware resource management moduleof one or more first nodes (as slave node(s)) (e.g., first node 1, first node 2, . . . , first node M, etc.). In some embodiments, in order to obtain the second processing capacities, the hardware resource management moduleof the second node (as the master node) may obtain fifth processing capacities from the hardware resource management module of one or more second nodes (e.g., the current master node, other second nodes as slave nodes, or the like, or a combination thereof) (e.g., second node 1, second node 2, . . . , second node N, etc.) of the distributed system. In some embodiments, the hardware resource management moduleof the second node (as the master node) may obtain processing capacities from one or more first nodes (as slave nodes) and one or more second nodes (e.g., the current master node, other second nodes as slave nodes, or the like, or a combination thereof). In some embodiments, the reconstruction and display modulemay be further configured to generate an image reconstruction scheme and/or assign one or more second sub tasks (and/or third sub tasks) to the distributed system. In some embodiments, in order to assign the second sub task(s) (and/or third sub task(s)) to the distributed system, the reconstruction and display modulemay assign (or provide, transmit, or send) one or more of the second sub task(s) (and/or third sub task(s)) and corresponding image raw data (e.g., the second image raw data, or fourth image raw data) to the control and data transmission interface. In some embodiments, the fourth image raw data may be a subset of the second image raw data. The control and data transmission interfaceof the second node may send the second sub tasks (and/or third sub tasks) and the fourth raw data to the control and data transmission interface of one or more first nodes. In some embodiments, if only one of the first nodes is needed to complete the reconstruction task, the fourth image raw data may be the same as the second image raw data.

9122 9122 9122 9122 In some embodiments, the control and data transmission interfaceof the second node may assign (or provide, transmit, or send) the second sub task(s) (and/or third sub task(s)) and the fourth image raw data to the control and data transmission interface of one or more second nodes of the distributed system. The control and data transmission interfacemay be further configured to receive one or more intermediate results (e.g., second intermediate results) fed back by the distributed system. Specifically, in some embodiments, the control and data transmission interfaceof the second node may receive second intermediate results fed back by the control and data transmission interface of the first node(s). In some embodiments, the control and data transmission interfaceof the second node may receive second intermediate results fed back by the control and data transmission interface of one or more second nodes of the distributed system.

9122 In some embodiments, in order to assign the second sub task(s) to the distributed system, the control and data transmission interfaceof the second node may assign, according to the characteristics of the processing capacities of the first node(s) and/or the second node(s), second sub task(s) that match the characteristics of the processing capacities to the first node(s) and/or the second node(s).

9110 9122 9122 In some embodiments, the computing devicemay be coupled to (e.g., connected to) one or more imaging devices, the control and data transmission interfacemay be further configured to receive image raw data (e.g., the second image raw data, the third image raw data, etc.). Specifically, the control and data transmission interfacemay receive image raw data acquired (or generated) by at least one imaging device. In some embodiments, the imaging device may include, but is not limited to, MRI and/or CT. In some embodiments, the image raw data may include a collection of one or more MRI data sets and/or a collection of one or more CT data sets.

9120 9110 In some embodiments, the service platformmay be configured to determine an individual scanning strategy for each of the imaging device(s) according to a property of each of the imaging device(s). In some embodiments, the individual scanning strategy may be adaptive to a displaying interface of the computing device. Thus, the service platform can configure different scanning strategies according to the self-adaptive displaying interface, thereby achieving better image display effects.

9130 9121 9130 9121 9121 9130 9130 9122 9122 In some embodiments, the hardware resources may include nonstandard resourcesthat are configured in a heterogeneous system architecture and are in communication with the hardware resource management module. In some embodiments, the nonstandard resourcesmay be configured to evaluate processing capacities of its hardware resources based on test case(s) provided by the hardware resource management module. In some embodiments, the hardware resource management modulemay be further configured to obtain sixth processing capacities of the nonstandard resourcesbased on the capacity quantization standard. In some embodiments, the nonstandard resourcesmay be further configured to receive fourth sub task(s) from the control and data transmission interface, and feed back (or provide) fourth intermediate result(s) corresponding to the fourth sub task(s) to the control and data transmission interface. In some embodiments, the image reconstruction apparatus(es) provided by the present disclosure can be coupled with (e.g., connected with) other brand(s) of image device(s) or workstation(s) with processing capacities through the control and data transmission interface. Therefore, the distributed system may be an open sharing system for sharing processing capacities between image device(s) of the same brand, and between image devices of different brands or manufacturers. In some embodiments, the manufacturer(s) of the workstation(s) or the imaging device(s), can also join the distributed system as the first node and/or the second node through the control and data transmission interface after installing the service platform, accepting processing capacity evaluation test(s) and passing corresponding certification(s). Further, each of the first node(s) and the second node(s) added to the service platform may have a unique ID, therefore, the distributed system of the present disclosure may not only provide a platform for sharing processing capacities but also provide a platform for sharing image information.

It should be noted that the above description is merely provided for the purpose of illustration, and not intended to limit the scope of the present disclosure. For persons having ordinary skills in the art, multiple variations and modifications may be made under the teachings of the present disclosure. However, those variations and modifications do not depart from the scope of the present disclosure. In some embodiments, the service platform of all nodes in the distributed system may include three modules: the control and data transmission interface, the hardware resource management module, and reconstruction and display module. For the master node, the reconstruction and display module may perform sub task allocation (or assigning) and reconstruction result generation (e.g., image synthesis). For a slave node, the reconstruction and display module may not be implemented or may not exist. Further, a master node and a slave node may be convertible, and a node that has a local task may function as the master node. In some embodiments, if a node does not have a workstation at all, it can only function as the master node, because it cannot function as a slave node and provide processing capacities to other master node(s). In some embodiments, a slave node may serve multiple master nodes at the same time. For any reconstruction task, there may be only one master node and one or more slave nodes.

10 FIG. 11 FIG. 12 FIG. 13 FIG. 10 FIG. 1000 700 800 900 800 900 is a block diagram illustrating an exemplary distributed system for image reconstruction according to some embodiments of the present disclosure.is a block diagram illustrating an exemplary service platform and a heterogeneous system architecture of hardware resources of a distributed system according to some embodiments of the present disclosure.is a flowchart illustrating an exemplary process for image reconstruction according to some embodiments of the present disclosure.is a flowchart illustrating an exemplary process for image reconstruction according to some embodiments of the present disclosure. As shown in, the distributed systemmay include at least two second nodes that are in communication with each other or at least one second node and one or more first nodes that are in communication with each other. In some embodiments, the first node may include or be an image reconstruction apparatus,or. The second node may include or be an image reconstruction apparatusor. In some embodiments, a second node may also be referred to as a master node of a service platform or a master node, and the first node may also be referred to as a slave node of the service platform or a slave node.

In some embodiments, the second node that has a need or request for image reconstruction (also referred to as target second node) may be configured to obtain processing capacities of one or more of the first node(s) and/or one or more of the second node(s) based on the received image raw data and/or the scan task (or information relating to the scan task) that generates the image raw data. In some embodiments, the second node may be further configured to generate an image reconstruction scheme (and/or one or more sub takes thereof) and/or assign the sub task(s) to one or more of the first node(s) and/or one or more of the second node(s) according to the processing capacities and/or the image raw data. The first node(s) and/or the second node(s) receiving the sub task(s) may generate intermediate result(s) corresponding to the sub task(s) and feed back (or provide) the intermediate result(s). The second node that has the need or request for image reconstruction (or the target second node) may be further configured to obtain the intermediate result(s), generate a reconstruction result according to the intermediate result(s), and/or display on a display device of the console computer.

In some embodiments, each of the first node(s) and the second node(s) may have a unique ID in the distributed system. With this configuration, the distributed system provided by the present disclosure can not only provide or serve as a platform for sharing processing capacities but also provide or serve as a platform for sharing image information.

According to the distributed system described in the present disclosure, the hardware resources of the image reconstruction apparatus(es) can be configured in a heterogeneous system architecture according to the computing requirements and/or actual working conditions, so as to realize the modularization of the hardware resources and/or the software programs, make the service platform evaluate processing capacities of the hardware resources accurately, and allocate image reconstruction sub tasks as required. The software implemented on different image reconstruction apparatus(es) is configured as a service platform. In some embodiments, the service platform may be general or universal, and may assign (or transmit), according to the available hardware resources, the sub task(s) through the network (e.g., a 5G network) to obtain the intermediate result(s). The manufacturer of the imaging device and/or other users can share processing capacities with paid through the 5G network. For workstations or hardware resources from other manufacturers, although the standardization degree of the workstations or hardware resources is relatively low, and can only be evaluated and certified for limited processing capacities, they can join the distributed system for sharing processing capacities if they are willing to use the general data interface provided by the service platform.

11 FIG. One of the scenarios of the distributed system for image reconstruction with shared processing capacities may be described below. As shown in, the service platform may be installed on the computing device (also referred to as the console computer). The console computer may be coupled to (e.g., connected to) or in communication with the front-end interface of an imaging device. For different imaging devices (e.g., MRI and CT), different scanning strategies may be determined through the adaptive graphical interface (or displaying interface) of the service platform. The image raw data generated in scanning is received through the control and data transmission interface provided by the console computer, which is different from the traditional way(s) to receive data from a workstation. The master node (i.e., the master node of reconstruction, (e.g., the second node)) of the service platform may assign at least a portion of image raw data to the slave node (i.e., the slave node of reconstruction, (e.g., the first node)) of the service platform. The slave node of the service platform may receive the at least a portion of the image raw data from the master node to perform corresponding sub tasks. Further, the service platform on the console computer may perform management functions of hardware resources. The reconstruction operations (e.g., reconstruction sub tasks) may be implemented on the hardware resources of the local master node and/or the hardware resources of other slave nodes over the network. In some embodiments, most of these hardware resources may be standardized and configured in a heterogeneous system architecture. In some embodiments, different hardware resources may be good at performing different sub tasks. The hardware resource management module of the service platform may detect available hardware resources, determine the latency of these hardware resources, transmission bandwidth(s) and/or price(s) of these hardware resources, and provide information for the reconstruction and display module to generate an appropriate reconstruction scheme. The hardware resource management module of the master node may obtain this information by communicating with the corresponding module of the slave node through a handshake mechanism. For non-standardized hardware resources, the service platform may provide test case(s) to evaluate the hardware resources. The slave node(s) may provide service quotation(s) according to the network status and its own resource quality.

The service platform of the master node may run the reconstruction and display module. The reconstruction and display module may generate a reconstruction scheme, based on information of the hardware resources obtained by the hardware resource management module, and/or the characteristics of the current reconstruction task. In some embodiments, users may select various schemes such as a time priority scheme, a cost priority scheme, a default scheme, etc. In some embodiments, different sub tasks (together with relevant image raw data) may be allocated according to different hardware resource characteristics, and transmitted to the local master node and/or slave node(s) through the control and data transmission interface. The node(s) that receive the sub task(s) may perform received sub tasks and generate intermediate result(s). The intermediate result(s) may be used to generate a reconstruction result. The reconstruction result may be displayed on the display device of the master node.

12 FIG. 7 FIG. 1201 1203 1205 1207 1301 1303 1305 1307 Specifically, as shown in, during image reconstruction, the second node may: in, set one or more scan tasks, and determine one or more reconstruction strategies; in, detect local and/or remote hardware resources; in, determine or generate, according to the hardware resources that can be obtained, a reconstruction scheme, and/or one or more sub task(s) of the reconstruction scheme, and allocate the sub tasks; in, receive (or manage) intermediate result(s) corresponding to the sub task(s), generate a reconstruction result (e.g., a reconstructed image) based on the intermediate result(s), and display the reconstruction result. Correspondingly, as shown in, during image reconstruction, the first node may: in, determine a price based on hardware resources and/or network resources; in, feed back (or provide) processing capacities, availabilities and/or accessibilities of the hardware resources (e.g., through a handshake mechanism); in, receive sub task(s) and related image raw data; in, feed back (or provide) intermediate result(s) corresponding to the sub task(s).

It should be noted that in a certain scenario, a node of the distributed system can be used as the first node, and in another scenario, the node can be used as the second node, which is not limited to the present disclosure. Further, those skilled in the art will understand that in some cases, a node may have its own hardware resources (e.g., it has a workstation) that can match the sub task(s) generated by the node itself. In some embodiments, the node may have dual attributes of the first node and the second node at the same time. Further, the present disclosure does not limit the number of the second nodes that share processing capacities with the first node(s) at the same time. That is, at the same time, according to the utilization of hardware resources and the need for image reconstruction, the first node may provide processing capacities for multiple second nodes.

Since the distributed system for image reconstruction provided by the present disclosure includes the image reconstruction apparatus(es) provided by the present disclosure, it has at least the same beneficial effects as the image reconstruction apparatus(es), and will not be repeated herein.

14 FIG. 15 FIG. 16 FIG. 14 FIG. is a flowchart illustrating an exemplary process for image reconstruction according to some embodiments of the present disclosure.is a flowchart illustrating an exemplary process for image reconstruction according to some embodiments of the present disclosure.is a flowchart illustrating an exemplary process for image reconstruction according to some embodiments of the present disclosure. In some embodiments, the distributed system for image reconstruction may include M first nodes and N second nodes that are in communication with each other, wherein M≥1 and N≥1, or M=0 and N≥2. As shown in, the image reconstruction process may include one or more of the following operations.

1401 In, the second node that has a need or request for image reconstruction (also referred to as a target second node), may obtain processing capacities of one or more nodes of the M first nodes and/or the N second nodes based on received image raw data and corresponding scan task(s).

1403 In, the target second node may generate, based on the processing capacities and the image raw data, a reconstruction task (or an image reconstruction scheme) including one or more sub tasks, and assign (or allocate) the sub task(s) to the one or more nodes.

1405 In, the one or more nodes that receive the sub task(s) may perform the sub task(s), generate one or more intermediate results of the one or more sub tasks, and feed back (or provide) the intermediate result(s) to the target second node.

1407 In, the target second node may generate a reconstructed image of the image raw data based on the intermediate result(s).

1405 1500 14 FIG. 15 FIG. 13 FIG. In some embodiments, in operationillustrated in, the slave node may perform operations according to the processof. As shown in, during the image reconstruction process, the node (i.e., the first node and/or the second node) may receive the sub task(s) and perform one or more of the following operations.

1501 In, the node may determine, based on the hardware resources at its disposal, and/or according to the capacity quantization standard, first processing capacities of hardware resources, and provide the first processing capacities for the image reconstruction service platform.

1503 In, the node may receive one or more first sub tasks and first image raw data relating to the first sub task(s), generate one or more intermediate results by performing, based on the first image raw data, the first sub task(s), and provide the intermediate result(s) for the image reconstruction service platform. In some embodiments, the first sub task(s) may include a sub task assigned by the second node of the distributed system. In some embodiments, the first image raw data may include image raw data sent by (or transmitted from) the second node.

1405 1600 14 FIG. 16 FIG. 16 FIG. In some embodiments, in operationillustrated in, the master node may perform operations according to the processof. As shown in, during the image reconstruction process, the target second node may perform one or more of the following operations.

1601 In, the target second node may obtain second processing capacities of the distributed system based on second image raw data and corresponding scan task(s).

1603 In, the target second node may generate, based on the second image raw data and the second processing capacities, a second reconstruction task including one or more second sub tasks, and assign the one or more second sub tasks to one or more nodes.

1605 In, the target second node may generate, based on one or more second intermediate results generated from the one or more second sub tasks, a reconstructed image of the second image raw data, and display the reconstructed image.

Having thus described the basic concepts, it may be rather apparent to those skilled in the art after reading this detailed disclosure that the foregoing detailed disclosure is intended to be presented by way of example only and is not limiting. Various alterations, improvements, and modifications may occur and are intended to those skilled in the art, though not expressly stated herein. These alterations, improvements, and modifications are intended to be suggested by this disclosure and are within the spirit and scope of the exemplary embodiments of this disclosure.

Moreover, certain terminology has been used to describe embodiments of the present disclosure. For example, the terms “one embodiment,” “an embodiment,” and/or “some embodiments” mean that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, it is emphasized and should be appreciated that two or more references to “an embodiment” or “one embodiment” or “an alternative embodiment” in various portions of this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures or characteristics may be combined as suitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects of the present disclosure may be illustrated and described herein in any of a number of patentable classes or context including any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof. Accordingly, aspects of the present disclosure may be implemented entirely hardware, entirely software (including firmware, resident software, micro-code, etc.) or combining software and hardware implementation that may all generally be referred to herein as a “unit,” “module,” or “system.” Furthermore, aspects of the present disclosure may take the form of a computer program product embodied in one or more computer readable media having computer readable program code embodied thereon.

A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including electro-magnetic, optical, or the like, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that may communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable signal medium may be transmitted using any appropriate medium, including wireless, wireline, optical fiber cable, RF, or the like, or any suitable combination of the foregoing.

2103 2102 Computer program code for carrying out operations for aspects of the present disclosure may be written in a combination of one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET, Python or the like, conventional procedural programming languages, such as the “C” programming language, Visual Basic, Fortran, Perl, COBOL, PHP, ABAP, dynamic programming languages such as Python, Ruby and Groovy, or other programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider) or in a cloud computing environment or offered as a service such as a Software as a Service (SaaS).

Each of the patents, patent applications, publications of patent applications, and other material, such as articles, books, specifications, publications, documents, things, and/or the like, referenced herein is hereby incorporated herein by this reference in its entirety for all purposes, excepting any prosecution file history associated with same, any of same that is inconsistent with or in conflict with the present document, or any of same that may have a limiting affect as to the broadest scope of the claims now or later associated with the present document. By way of example, should there be any inconsistency or conflict between the description, definition, and/or the use of a term associated with any of the incorporated material and that associated with the present document, the description, definition, and/or the use of the term in the present document shall prevail.

In closing, it is to be understood that the embodiments of the application disclosed herein are illustrative of the principles of the embodiments of the application. Other modifications that may be employed may be within the scope of the application. Thus, by way of example, but not of limitation, alternative configurations of the embodiments of the application may be utilized in accordance with the teachings herein. Accordingly, embodiments of the present application are not limited to that precisely as shown and described.